scholarly journals Coarse2Fine: a two-stage training method for fine-grained visual classification

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Amir Erfan Eshratifar ◽  
David Eigen ◽  
Michael Gormish ◽  
Massoud Pedram
Keyword(s):  
Training Method ◽  
Two Stage ◽  
Visual Classification ◽  
Fine Grained

scholarly journals AP-CNN: Weakly Supervised Attention Pyramid Convolutional Neural Network for Fine-Grained Visual Classification

2021 ◽  
Vol 30 ◽  
pp. 2826-2836 ◽  
Author(s):  
Yifeng Ding ◽  
Zhanyu Ma ◽  
Shaoguo Wen ◽  
Jiyang Xie ◽  
Dongliang Chang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Visual Classification ◽  
Fine Grained ◽  
Weakly Supervised

scholarly journals Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis

2020 ◽  
Vol 34 (05) ◽  
pp. 8600-8607
Author(s):  
Haiyun Peng ◽  
Lu Xu ◽  
Lidong Bing ◽  
Fei Huang ◽  
Wei Lu ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Complete Solution ◽  
Unified Model ◽  
Two Stage ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Second Stage ◽  
Opinion Extraction ◽  
Complete Story

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from “Waiters are very friendly and the pasta is simply average” could be (‘Waiters’, positive, ‘friendly’). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.

Two-stage local constrained sparse coding for fine-grained visual categorization

2017 ◽  
Vol 61 (1) ◽  
Author(s):  
Lihua Guo ◽  
Chenggang Guo ◽  
Lei Li ◽  
Qinghua Huang ◽  
Yanshan Li ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Two Stage ◽  
Visual Categorization ◽  
Fine Grained

Errata for "Cerebro: a data system for optimized deep learning model selection"

2021 ◽  
Vol 14 (6) ◽  
pp. 863-863
Author(s):  
Supun Nakandala ◽  
Yuhao Zhang ◽  
Arun Kumar
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Learning Model ◽  
Communication Cost ◽  
Data System ◽  
Training Method ◽  
Fine Grained ◽  
Correct Communication ◽  
Deep Learning Model

We discovered that there was an inconsistency in the communication cost formulation for the decentralized fine-grained training method in Table 2 of our paper [1]. We used Horovod as the archetype for decentralized fine-grained approaches, and its correct communication cost is higher than what we had reported. So, we amend the communication cost of decentralized fine-grained to [EQUATION]

Pose Aware Fine-Grained Visual Classification Using Pose Experts

2018 ◽  
Author(s):  
Kushagra Mahajan ◽  
Tarasha Khurana ◽  
Ayush Chopra ◽  
Isha Gupta ◽  
Chetan Arora ◽  
...  
Keyword(s):  
Visual Classification ◽  
Fine Grained
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